Tremendous gains have been made in recent years towards achieving the goal of performing autonomous navigation of micro air vehicles (MAV), however several considerable challenges remain. Perhaps the most significant obstacle is that satellite based navigation aids such as the Global Position System (GPS) may either perform poorly or be altogether unavailable when used for indoor environments. An inertial navigation system is effective for indoor navigation applications because these systems do not rely on the reception of external signals and newer models are improving in performance while decreasing in size. However, smaller inertial navigation systems generally require additional sensing capabilities to correct inherent instabilities if they are to be used for an extended period of time. A camera based vision system offers the necessary sensing capabilities in a form that is increasingly available with greater imaging performance and lower size, weight and power requirements. A primary impediment to the adoption of vision aided inertial navigation systems however is ambiguity regarding scale.
Loss of access to the GPS through interference from disturbances from the ionosphere, competing radio signals, or obstruction by urban structures is a significant challenge for a modern vehicle navigation system to overcome. When this challenge is anticipated, a favored solution is reliance on a self-contained inertial navigation system (INS). When an INS is set or initialized to correct vehicle location and pose estimates, navigation errors are often small, such that they may be neglected for short periods of time. Errors, however, can grow in time without bound such that an uncorrected INS becomes unstable. An INS is often supplemented by complimentary sensor systems to correct navigation errors such that they remain relatively small. Camera based sensing systems have been used to provide correction to an INS by exploiting the persistence of image features that are detectable from different perspectives. These systems use multiple camera images for feature heading measurements to estimate constraints to either the navigation solution or the slant range to the features. What is missing is an INS solution using a camera system that captures both heading and slant range to each feature in a single image, thereby enabling estimation of the location of the feature relative to the vehicle with just one camera.
From a single conventional image, a vision system can be used to determine the heading from the camera to a feature within a scene; however the range to the feature cannot be determined without a priori information. This ambiguity frustrates estimation of the location of the feature in the global coordinate frame. A common approach to this problem is triangulation of image feature location by observing the feature from different perspectives by one or more cameras. Using more than one camera for a micro air vehicle is not desirable, as each additional camera adds weight and consumes power while the baseline distance between any pair of cameras would be necessarily small. Triangulation from a single moving camera is also not desirable, as the estimated distances between any two locations from which the perspective images are captured are determined by the navigation solutions that are to be solved. Other methods include the exploitation of a priori information, such as an expectation of the height above the ground, the shape of the horizon, or a mapping of the region to be traversed.